Journal
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Volume 19, Issue 5, Pages 906-912Publisher
OXFORD UNIV PRESS
DOI: 10.1136/amiajnl-2011-000591
Keywords
-
Categories
Funding
- National Institutes of Health (NIH)/National Library of Medicine (NLM) [2U54LM008748]
- National Heart, Lung and Blood Institute (NHLBI)
- Office of the National Coordinator (ONC) for Health Information Technology [90TR0002]
- National Institute of Biomedical Imaging and Bioengineering [EB001659]
Ask authors/readers for more resources
Objective Narratives of electronic medical records contain information that can be useful for clinical practice and multi-purpose research. This information needs to be put into a structured form before it can be used by automated systems. Coreference resolution is a step in the transformation of narratives into a structured form. Methods This study presents a medical coreference resolution system (MCORES) for noun phrases in four frequently used clinical semantic categories: persons, problems, treatments, and tests. MCORES treats coreference resolution as a binary classification task. Given a pair of concepts from a semantic category, it determines coreferent pairs and clusters them into chains. MCORES uses an enhanced set of lexical, syntactic, and semantic features. Some MCORES features measure the distance between various representations of the concepts in a pair and can be asymmetric. Results and Conclusion MCORES was compared with an in-house baseline that uses only single-perspective 'token overlap' and 'number agreement' features. MCORES was shown to outperform the baseline; its enhanced features contribute significantly to performance. In addition to the baseline, MCORES was compared against two available third-party, open-domain systems, RECONCILEACL09 and the Beautiful Anaphora Resolution Toolkit (BART). MCORES was shown to outperform both of these systems on clinical records.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available